QTL detection for a medium density SNP panel: comparison of different LD and LA methods

Background: New molecular technologies allow high throughput genotyping for QTL mapping with dense genetic maps. Therefore, the interest of linkage analysis models against linkage disequilibrium could be questioned. As these two strategies are very sensitive to marker density, experimental design structures, linkage disequilibrium extent and QTL effect, we propose to investigate these parameters effects on QTL detection.[br/] Methods: The XIIIth QTLMAS workshop simulated dataset was analysed using three linkage disequilibrium models and a linkage analysis model. Interval mapping, multivariate and interaction between QTL analyses were performed using QTLMAP.[br/] Results: The linkage analysis models identified 13 QTL, from which 10 mapped close of the 18 which were simulated and three other positions being falsely mapped as containing a QTL. Most of the QTLs identified by interval mapping analysis are not clearly detected by any linkage disequilibrium model. In addition, QTL effects are evolving during the time which was not observed using the linkage disequilibrium models.[br/] Conclusions: Our results show that for such a marker density the interval mapping strategy is still better than using the linkage disequilibrium only. While the experimental design structure gives a lot of power to both approaches, the marker density and informativity clearly affect linkage disequilibrium efficiency for QTL detection

Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

<p>Abstract</p> <p>Background</p> <p>The QTLMAS XV^thdataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV) were evaluated by participants of the XV^thQTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop.</p> <p>Methods</p> <p>From the known QTL genotypes of each candidate, two "true" genomic values (TV) were estimated by organizers: the genotypic value of the candidate (TGV) and the expectation of its progeny genotypic values (TBV). GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression), selection variable techniques (LASSO, Elastic Net) and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV) and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV.</p> <p>Results</p> <p>Large differences between methods were found for all criteria and type of genetic values (TGV, TBV). In general, the criteria ranked consistently methods belonging to the same family.</p> <p>Conclusions</p> <p>Bayesian methods - A<B<C<Cπ - were the most efficient whatever the criteria and the True Value considered (with the notable exception of the MSEP of the TBV). The selection variable procedures (LASSO, Elastic Net and some adaptations) performed similarly, probably at a much lower computing cost. The TABLUP, which combines BayesB and GBLUP, generally did well. The simplest methods, GBLUP or Ridge Regression, and even worst, the fixed linear model, were much less efficient.</p

High resolution physical map of porcine chromosome 7 QTL region and comparative mapping of this region among vertebrate genomes

BACKGROUND: On porcine chromosome 7, the region surrounding the Major Histocompatibility Complex (MHC) contains several Quantitative Trait Loci (QTL) influencing many traits including growth, back fat thickness and carcass composition. Previous studies highlighted that a fragment of ~3.7 Mb is located within the Swine Leucocyte Antigen (SLA) complex. Internal rearrangements of this fragment were suggested, and partial contigs had been built, but further characterization of this region and identification of all human chromosomal fragments orthologous to this porcine fragment had to be carried out. RESULTS: A whole physical map of the region was constructed by integrating Radiation Hybrid (RH) mapping, BAC fingerprinting data of the INRA BAC library and anchoring BAC end sequences on the human genome. 17 genes and 2 reference microsatellites were ordered on the high resolution IMNpRH2(12000rad )Radiation Hybrid panel. A 1000:1 framework map covering 550 cR(12000 )was established and a complete contig of the region was developed. New micro rearrangements were highlighted between the porcine and human genomes. A bovine RH map was also developed in this region by mapping 16 genes. Comparison of the organization of this region in pig, cattle, human, mouse, dog and chicken genomes revealed that 1) the translocation of the fragment described previously is observed only on the bovine and porcine genomes and 2) the new internal micro rearrangements are specific of the porcine genome. CONCLUSION: We estimate that the region contains several rearrangements and covers 5.2 Mb of the porcine genome. The study of this complete BAC contig showed that human chromosomal fragments homologs of this heavily rearranged QTL region are all located in the region of HSA6 that surrounds the centromere. This work allows us to define a list of all candidate genes that could explain these QTL effects

Using transcriptome profiling to characterize QTL regions on chicken chromosome 5

<p>Abstract</p> <p>Background</p> <p>Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5).</p> <p>Results</p> <p>Hepatic transcriptome profiles for 45 offspring of a sire known to be heterozygous for the distal GGA5 AF QTL were obtained using a 20 K chicken oligochip. mRNA levels of 660 genes were correlated with the AF trait. The first approach was to dissect the AF phenotype by identifying animal subgroups according to their 660 transcript profiles. Linkage analysis using some of these subgroups revealed another QTL in the middle of GGA5 and increased the significance of the distal GGA5 AF QTL, thereby refining its localization. The second approach targeted the genes correlated with the AF trait and regulated by the GGA5 AF QTL region. Five of the 660 genes were considered as being controlled either by the AF QTL mutation itself or by a mutation close to it; one having a function related to lipid metabolism (HMGCS1). In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region. The third approach was to use these 5 transcriptome profiles to predict the paternal Q versus q AF QTL mutation for each recombinant offspring and then refine the localization of the QTL from 31 cM (100 genes) at a most probable location confidence interval of 7 cM (12 genes) after determining the recombination breakpoints, an interval consistent with the reductions obtained by the two other approaches.</p> <p>Conclusion</p> <p>The results showed the feasibility and efficacy of the three strategies used, the first revealing a QTL undetected using the whole population, the second providing functional information about a QTL region through genes related to the trait and controlled by this region (HMGCS1), the third could drastically refine a QTL region.</p

Development and validation of high-density SNP array in ducks

Development and validation of high-density SNP array in ducks. XIth European symposium on poultry genetics (ESPG

Microbiota and Metabolite Profiling as Markers of Mood Disorders: A Cross-Sectional Study in Obese Patients

Obesity is associated with an increased risk of several neurological and psychiatric diseases, but few studies report the contribution of biological features in the occurrence of mood disorders in obese patients. The aim of the study is to evaluate the potential links between serum metabolomics and gut microbiome, and mood disturbances in a cohort of obese patients. Psychological, biological characteristics and nutritional habits were evaluated in 94 obese subjects from the Food4Gut study stratified according to their mood score assessed by the Positive and Negative Affect Schedule (PANAS). The fecal gut microbiota and plasma non-targeted metabolomics were analysed. Obese subjects with increased negative mood display elevated levels of Coprococcus as well as decreased levels of Sutterella and Lactobacillus. Serum metabolite profile analysis reveals in these subjects altered levels of several amino acid-derived metabolites, such as an increased level of L-histidine and a decreased in phenylacetylglutamine, linked to altered gut microbiota composition and function rather than to differences in dietary amino acid intake. Regarding clinical profile, we did not observe any differences between both groups. Our results reveal new microbiota-derived metabolites that characterize the alterations of mood in obese subjects, thereby allowing to propose new targets to tackle mood disturbances in this context. Food4gut, clinicaltrial.gov: NCT03852069

Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

<p>Abstract</p> <p>Background</p> <p>Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.</p> <p>In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM.</p> <p>Results</p> <p>Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region.</p> <p>Conclusion</p> <p>Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.</p